Optimization of Packed-Bed Energy Storage Systems Based on a Second Law Analysis

TL;DR

This paper applies second law analysis to optimize packed-bed sensible heat storage systems, identifying internal losses and proposing design improvements to reduce exergy destruction and enhance efficiency.

Contribution

It develops macroscopic entropy and exergy transport equations for porous media and demonstrates their application in optimizing a packed-bed SHS system.

Findings

Optimal tank aspect ratio of D/H=0.75 minimizes exergy loss.

Decreasing particle size reduces exergy loss coefficient.

Optimized design reduces exergy loss from 4.9% to 4.1%.

Abstract

Packed-bed sensible heat storage (SHS) is important for balancing energy supply and demand over time. To improve the efficiency of a packed-bed SHS system through second law analysis (SLA), we developed macroscopic entropy and exergy transport equations for fluid flow and heat transfer in porous media based on microscopic transport equations. These equations enable us to identify where and how much exergy is destroyed. Using a packed-bed SHS system developed at the PROMES-CNRS laboratory as a test case, we demonstrated how to apply SLA to optimize an SHS system. Our analysis revealed that, in addition to exit and heat leakage losses at tank surfaces, thermal and solid conduction losses inside the tank significantly contribute to total loss in the studied SHS system. These internal losses occur close to the thermocline. However, their slower transport causes a delay in their emergence. The SLA suggests an optimal tank aspect ratio of D/H = 0.75, at which the total exergy loss coefficient reaches its minimum value when exit loss is not considered. As particle size decreases, the exergy loss coefficient also decreases due to enhanced heat transfer between the fluid and solid phases. The pressure loss for the studied SHS system is negligible. The SLA favors a truncated cone-shaped tank with a slightly larger upper surface. Through the SLA, the exergy loss coefficient is reduced from 4.9% for the original design to 4.1% for the optimized design. This study demonstrates that, when used in conjunction with energy analysis, the SLA is an effective tool for optimizing energy storage systems.